• Suche: Python for deep learning

Treffer 121 - 140 von 22.975

125

1. American Heart Association. (2021). Heart disease and stroke statistics—2021 update. Circulation, 143(8), e254-e743. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. 5. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, i2416. 6. Framingham Heart Study. (1948). Framingham Heart Study cohort research data. National Heart, Lung, and Blood Institute. 7. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. 8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. 9. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). 10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 11. Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart, 104(14), 1156-1164. 12. Steyerberg, E. W., Vergouwe, Y., & van Calster, B. (2019). Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal, 40(15), 1255–1264. 13. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. 14. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., ... Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 17. Chollet, F. (2015). Keras (Version 2.4.0) [Computer software]. https://github.com/fchollet/keras
Okunola, Abiodun

Zu den Favoriten
127

VERONA: A python library for benchmarking deep learning in business process monitoring
Pedro Gamallo-Fernandez ; Efrén Rama-Maneiro ; Juan C. Vidal ; et al.
SoftwareX, Vol 26, Iss , Pp 101734- (2024)

Process mining Predictive process monit... Benchmarking Deep learning Computer software QA76.75-76.765
Fachzeitschrift
Zu den Favoriten
128

Automatic feature extraction using deep learning for automatic modulation classification implemented with Python
Nakul Kishor Pathak ; Varun Bajaj
Signal Processing with Python ISBN: 9780750359290

Buch
Zu den Favoriten
129

Image segmentation techniques using python and deep learning
Mayank Pandey ; Anuwanshi Sharma
International Journal of Communication and Information Technology. 4:18-25

Fachzeitschrift
Zu den Favoriten
130

AUDIT: An open-source Python library for AI model evaluation with use cases in MRI brain tumor segmentation
Aumente-Maestro, Carlos ; Müller, Michael ; Remeseiro, Beatriz ; et al.
In Computer Methods and Programs in Biomedicine November 2025 271

Fachzeitschrift
Zu den Favoriten
131

PhenoAI: A deep learning Python framework to process close-range time-lapse PhenoCam data
Akash Kumar ; Siddhartha Khare ; Sergio Rossi
Ecological Informatics, Vol 88, Iss , Pp 103134- (2025)

Close-range Remote Sensi... GCC Forest Phenology PhenoCam Python Deep Learning …
Fachzeitschrift
Zu den Favoriten
132

GTS Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python.
Xuechen Liang ; Xiaoxing He ; Shengdao Wang ; et al.
CoRR. abs/2509.10560

Fachzeitschrift
Zu den Favoriten
133

Deep Learning-Based Digitization of Overlapping ECG Images with Open-Source Python Code.
Reza Karbasi ; Masoud Rahimi 0002 ; Abdol-Hossein Vahabie ; et al.
CoRR. abs/2506.10617

Fachzeitschrift
Zu den Favoriten
134

si4onnx: A Python package for Selective Inference in Deep Learning Models.
Teruyuki Katsuoka ; Tomohiro Shiraishi ; Daiki Miwa ; et al.
CoRR. abs/2501.17415

Fachzeitschrift
Zu den Favoriten
135

RESEARCH ON SIGNATURE RECOGNITION METHOD BASED ON DEEP LEARNING TECHNIQUE IN PYTHON LANGUAGE
Nguyen, Phuc Hau

Identity authentication Signature recognition Recurrent neural network Deep learning Convolutional neural net... Data preprocessing …
Report
Zu den Favoriten
136

MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras
Kensert, Alexander ; Desmet, Gert ; Cabooter, Deirdre
Journal of Computer-Aided Molecular Design: Incorporating Perspectives in Drug Discovery and Design. 39(1)

Fachzeitschrift
Zu den Favoriten
137

Eisen: a python package for solid deep learning
Mancolo, Frank

Computer Science - Compu... Electrical Engineering a...
Report
Zu den Favoriten
138

MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning
Sanchez-Garcia, Ruben ; Segura, Joan ; Maluenda, David ; et al.
In Journal of Structural Biology 1 June 2020 210(3)

Fachzeitschrift
Zu den Favoriten
139

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
Jamasb A. R. ; Vinas R. ; Ma E. J. ; et al.

Bioinformatics Deep learning Geometry Graph theory High level languages Learning systems …
Konferenz
Zu den Favoriten
Suchwerkzeuge: RSS-Feed abonnieren Diese Suche als E-Mail versenden Suche speichern

Filter